On the moment-recovered approximations of regression and derivative functions with applications

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• 作者：Robert M. Mnatsakanov ; ^{rmnatsak@stat.wvu.edu} ; Broti Garai
• 关键词：Moment-determinate functions ; Laplace transform inversion ; Nonparametric estimation
• 刊名：Journal of Computational and Applied Mathematics
• 出版年：2017
• 出版时间：1 May 2017
• 年：2017
• 卷：315
• 期：Complete
• 页码：17-31
• 全文大小：770 K
• 卷排序：315

文摘

In this paper three formulas for recovering the conditional mean and conditional variance based on product moments are proposed. The upper bounds for the uniform rate of approximations of regression and derivatives of some moment-determinate function are derived. Two cases where the support of underlying functions is bounded and unbounded from above are studied. Based on the proposed approximations, novel nonparametric estimates of the distribution function and its density in multiplicative-censoring model are constructed. Simulation study justifies the consistency of the estimates.